Prediction-Based Adaptive Designs for Reducing Wave Nonresponse Rates and Bias in Panel Surveys
Bibliographic Data
| ID | 24212967 |
|---|---|
| Authors | John T Collins (0000-0003-0282-927X, Australian National University), Saskia Bartholomäus (0000-0002-0083-1539, GESIS - Leibniz Institute for the Social Sciences), Tobias Gummer (0000-0001-6469-7802, GESIS - Leibniz Institute for the Social Sciences), Bernd Weiß (0000-0002-1176-8408, University of Duisburg-Essen), Christoph Kern (0000-0001-7363-4299, Ludwig-Maximilians-Universität München) |
| Year | 2026 |
| Publication date | 2026-09-29 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sociological Methods & Research (JOURNAL) |
| Journal identifiers | ISSN: 0049-1241 • E-ISSN: 1552-8294 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/00491241261492217 |
| OpenAlex | W7204894017 |
| Language | EN |
| References cited | 47 |
Machine learning (ML)-based nonresponse prediction in panel surveys enables selective interventions. However, the optimal use of ML predictions in Adaptive Survey Design (ASD) remains uncertain. We propose a method that integrates field experiment results on incentives, questionnaire length, and questionnaire content with ML-based propensity models to simulate ASD strategies ex-post with minimal assumptions. Using German panel data, we show that treating 15 percent of panelists, selected through ML-based predictions and R-indicator analysis, with increased cash incentives or a survey module on the respondents’ preferred topic can reduce nonresponse rates by up to 2 percentage points. These strategies also reduced bias in selected variables. We present our method as a framework for survey researchers to evaluate the expected outcomes of multiple ASD regimes simultaneously in a realistic setting. The framework helps identify both the criteria for allocating treatments to specific participant groups and the most effective modifications to the survey protocol.
nonresponse · nonresponse bias · Panel survey · respondent attrition · survey design · Census and Population Estimation · Survey Methodology and Nonresponse · Survey Sampling and Estimation Techniques
Fixed Effects Regression Models
The power of online panel paradata to predict unit nonresponse and voluntary attrition in a longitudinal design
Adaptive Survey Design
Methodology of Longitudinal Surveys
Applied Panel Data Analysis for Economic and Social Surveys
Randomized experiment on the effect of incentives and mailing strategy on response rates in a mail survey of dentists
A Note on How Prior Survey Experience With Self-Administered Panel Surveys Affects Attrition in Different Modes
Establishing an Open Probability-Based Mixed-Mode Panel of the General Population in Germany
Analyzing Nonresponse in Longitudinal Surveys Using Bayesian Additive Regression Trees
Using Paradata to Predict and Correct for Panel Attrition
Using Double Machine Learning to Understand Nonresponse in the Recruitment of a Mixed-Mode Online Panel
Experimental Evidence on Reducing Nonresponse Bias through Case Prioritization
Effects of Questionnaire Length on Participation and Indicators of Response Quality in a Web Survey
How Much Gets You How Much? Monetary Incentives and Response Rates in Household Surveys
Nonresponse Rates and Nonresponse Bias in Household Surveys
Are Incentive Effects on Response Rates and Nonresponse Bias in Large-scale, Face-to-face Surveys Generalizable to Germany? Evidence from Ten Experiments
Targeted Appeals for Participation in Letters to Panel Survey Members
The Additional Effects of Adaptive Survey Design Beyond Post-Survey Adjustment
| Citation velocity | historical |
|---|---|
| Highly cited | No |